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There are lots of command lines which can be used with the Google Chrome browser. Some change behavior of features, others are for debugging or experimenting. This page lists the available switches including their conditions and descriptions. Last automated update occurred on 2018-10-20.

Condition Explanation
-- Report pseudo allocation traces. Pseudo traces are derived from currently active trace events.
--/prefetch:1 /prefetch:# arguments to use when launching various process types. It has been observed that when file reads are consistent for 3 process launches with the same /prefetch:# argument, the Windows prefetcher starts issuing reads in batch at process launch. Because reads depend on the process type, the prefetcher wouldn't be able to observe consistent reads if no /prefetch:# arguments were used. Note that the browser process has no /prefetch:# argument; as such a
@nicjac
nicjac / zte_router_hack.js
Last active April 26, 2026 21:55
MC801a Javascript "Hack" (credits to MioNonno)
javascript: ftb();
function getStatus() {
$.ajax({
type: "GET",
url: "/goform/goform_get_cmd_process",
data: {
cmd: "lte_pci,lte_pci_lock,lte_earfcn_lock,wan_ipaddr,wan_apn,pm_sensor_mdm,pm_modem_5g,nr5g_pci,nr5g_action_channel,nr5g_action_band,Z5g_SINR,Z5g_rsrp,wan_active_band,wan_active_channel,wan_lte_ca,lte_multi_ca_scell_info,cell_id,dns_mode,prefer_dns_manual,standby_dns_manual,network_type,rmcc,rmnc,lte_rsrq,lte_rssi,lte_rsrp,lte_snr,wan_lte_ca,lte_ca_pcell_band,lte_ca_pcell_bandwidth,lte_ca_scell_band,lte_ca_scell_bandwidth,lte_ca_pcell_arfcn,lte_ca_scell_arfcn,wan_ipaddr,static_wan_ipaddr,opms_wan_mode,opms_wan_auto_mode,ppp_status,loginfo",
multi_data: "1",
},
dataType: "json",
/*
*
* Original code by Miononno
* https://www.youtube.com/watch?v=1kanq1w2DA0
*
* Enhanced by unknown @ lteforum.at
*
*/
console.log("Loading ZTE Script v" + "2025-04-17-#1");

LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

@rohitg00
rohitg00 / llm-wiki.md
Last active April 26, 2026 21:54 — forked from karpathy/llm-wiki.md
LLM Wiki v2 — extending Karpathy's LLM Wiki pattern with lessons from building agentmemory

LLM Wiki v2

A pattern for building personal knowledge bases using LLMs. Extended with lessons from building agentmemory, a persistent memory engine for AI coding agents.

This builds on Andrej Karpathy's original LLM Wiki idea file. Everything in the original still applies. This document adds what we learned running the pattern in production: what breaks at scale, what's missing, and what separates a wiki that stays useful from one that rots.

What the original gets right

The core insight is correct: stop re-deriving, start compiling. RAG retrieves and forgets. A wiki accumulates and compounds. The three-layer architecture (raw sources, wiki, schema) works. The operations (ingest, query, lint) cover the basics. If you haven't read the original, start there.

@VivianBalakrishnan
VivianBalakrishnan / VB-NANOCLAW-MEMORY-OBSI-WIKI-PUBLIC.md
Created April 24, 2026 09:34
NanoClaw — Personal Claude Assistant (second brain for a diplomat)

NanoClaw — Personal Claude Assistant

A self-hosted, compounding-memory AI assistant running on a Raspberry Pi.


What Is This?

NanoClaw is a personal AI assistant built on Anthropic's Claude that runs entirely on a Raspberry Pi. It connects to messaging channels (WhatsApp, Telegram, Slack, Discord), processes voice and images, schedules recurring tasks, and — unlike a standard chatbot — accumulates knowledge over time through a structured memory system.